Unmanned aerial vehicles (UAVs) can be used as air base stations to provide fast wireless connections for ground users. Due to their constraints on both mobility and energy consumption, a key problem is how to deploy UAVs adaptively in a geographic area with changing traffic demand of mobile users, while meeting the aforemetioned constraints. In this paper, we propose an adaptive deployment strategy for UAV-aided networks based on hybrid deep reinforcement learning, where a UAV can adjust its movement direction and distance to serve users who move randomly in the target area. Through hybrid deep reinforcement learning, UAVs can be trained offline to obtain the global state information and learn a completely distributed control strategy, with which each UAV only needs to take actions based on its observed state in the real deployment to be fully adaptive. Moreover, in order to improve the speed and effect of learning, we improve hybrid reinforcement learning, by adding genetic algorithms and TD-error-based resampling optimization mechanism. Simulation results show that the hybrid deep reinforcement learning algorithm has better efficiency and robustness in multi-UAV control, and has better performance in terms of coverage, energy consumption and average throughput, by which average throughput can be increased by 20% to 60%.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Adaptive Deployment of UAV-Aided Networks Based on Hybrid Deep Reinforcement Learning


    Beteiligte:
    Ma, Xiaoyong (Autor:in) / Hu, Shuting (Autor:in) / Zhou, Danyang (Autor:in) / Zhou, Yi (Autor:in) / Lu, Ning (Autor:in)


    Erscheinungsdatum :

    01.11.2020


    Format / Umfang :

    1949878 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep Reinforcement Learning based Adaptive Transmission Control in Vehicular Networks

    Liu, Mingyuan / Quan, Wei / Yu, Chengxiao et al. | IEEE | 2021


    Adaptive task offloading in V2X networks based on deep reinforcement learning

    Yang, Dengsong / Ni, Baili / Qin, Hao et al. | British Library Conference Proceedings | 2022


    Deep Reinforcement Learning-Based Resource Allocation for Secure RIS-aided UAV Communication

    Iqbal, Amjad / Al-Habashna, Ala'a / Wainer, Gabriel et al. | IEEE | 2023


    Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks

    Bohra, Asha Rani / Selvi, Thamarai / Vishal, V et al. | IEEE | 2025